基于最小最大核K均值聚类算法的水电机组振动故障诊断

Vibrant fault diagnosis for hydro-turbine generating unit using minmax kernel K-means clustering algorithm

基于聚类分析的故障诊断方法能够按照故障样本之间的相似性无监督地将同类故障聚为一簇,当前已成为一类有效的故障诊断策略。为解决传统聚类算法受初始聚类中心的影响,易陷入局部最优解的问题,提出一种最小最大核K均值聚类方法。该方法在聚类过程中为簇内方差赋以与其大小成正比的自动修正的权重,并引入核函数技术以处理低维输入空间的线性不可分问题,大大提高了聚类的精确性。在标准数据上将所提方法与标准K-means及K-means++比较,显示了所提算法的有效性和优越性。基于这一聚类方法提出了一种具有自学习能力的故障诊断模型。将该诊断模型应用于水电机组振动故障诊断,实例验证了模型的可行性。

Fault diagnosis methods based on clustering analysis can cluster the fault samples into a certain class according to their similarities without supervision, and thus become one type of effective fault diagnosis strategy. To overcome the problem that traditional clustering methods are susceptible to the initial clustering centers, and thus poor local optima is easily obtained, a MinMax kernel K-means clustering algorithm is introduced. In the proposed method, clusters are assigned weights relative to their variances. And kernel trick is introduced to deal with linear inseparable problem in input space. The proposed method is compared with the traditional K-means and K-means＋＋ in some international standard datasets. The comparison results show its effectiveness and advantage. Then, a fault diagnostic model based on MinMax kernel K-means clustering algorithm is proposed. At last, the fault diagnosis model is applied in fault diagnosis for hydro-turbine generating unit. The results illustrate the effectiveness of the proposed method.